Suicide prevention in pharmaceutical education: Raising awareness with inspiring stories
Bibliographic record
Abstract
Suicide rates are high in high-income countries like Canada and the United States, where 10 to 12 people per 100 000 commit suicide every year. In the United States, in 2011 there were 73.3 emergency room visits per 100 000 people for suicide attempts with prescription drugs. The latter were also involved in 13% of completed suicides between 1999 and 2013. In most cases, these drugs were distributed by members of our profession who could not predict this outcome. This led us to create an initiative to teach pharmacy students how to prevent suicide. A literature review and online search were performed to find documentation about pharmacists' commitment to the cause, but very little information exists. Thus, a training session was developed for third-year pharmacy students that includes basic statistics, arguments for involving pharmacists in suicide prevention, role-playing, tools to evaluate suicide risk, thoughtful verbatims of interview techniques, and case studies. It is delivered during the mental health theme of the psychiatry course. In 5 years, around 1150 students have participated in the course, of whom approximately 950 are now practicing pharmacists. This intervention may have prevented some suicides, although the impact is impossible to measure. The objective of this paper is to describe the creative process of designing a suicide prevention training session for pharmacy students, while inspiring a mental health sensitive readership to this noble cause. This article does not provide guidelines on how to replicate this initiative, nor does this article replace proper training on suicide prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".